The journal publishes research on statistical inference, signal processing, and machine learning, with a focus on theoretical guarantees and algorithmic development. Topics include sparse signal recovery, graphical models, neural network analysis, optimization methods, and high-dimensional probability. Many articles investigate theoretical properties such as convergence rates, error bounds, and asymptotic behavior of estimators and algorithms.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
AnalysisQ1
Applied MathematicsQ1
Computational Theory and MathematicsQ1
Numerical AnalysisQ1
Statistics and ProbabilityQ1
Subject Classification
Web of Science Categories
Mathematics, Applied
Scopus Categories
Applied MathematicsNumerical AnalysisComputational Theory and MathematicsAnalysisStatistics and Probability
Research Topics (OpenAlex)
Sparse and Compressive Sensing TechniquesStatistical Methods and InferenceBlind Source Separation TechniquesStochastic Gradient Optimization TechniquesMarkov Chains and Monte Carlo MethodsImage and Signal Denoising MethodsBayesian Methods and Mixture ModelsMicrowave Imaging and Scattering AnalysisMachine Learning and AlgorithmsRandom Matrices and Applications